Organizations often need to perform computationally-intensive tasks in their day-to-day business activities. For example, a bank may need to compute thousands of pricing models (e.g., forward contracts) within a short amount of time. In some instances, to facilitate the expedited processing of such computing tasks, some organizations may employ parallel processing techniques that enable the tasks to be executed concurrently, for example, by dividing the tasks across a parallel ized computing environment (e.g., graphics processing units (GPUs), multi-core processors, and/or compute nodes in a cluster). Such parallelization can require use of complex frameworks, such as OpenCL or Compute Unified Device Architecture (CUDA).
In many instances, a task, or parameters of the task, can be defined by entities that are different from the entities that are involved with the parallelization of that task. In one example, financial analysts may be involved with defining the terms of a deal or pricing model. However, given the complexity of parallelization frameworks, translating such deals or pricing models to be executed concurrently is typically a job that is reserved for software engineers. Such division of labor can create unnecessary bottlenecks in the day-to-day operation of a business, since any programs created by software engineers will often need to be validated, for example, by financial analysts.